README.md

---
language:
- en
license: gpl-3.0
tags:
- code
- text-only
- training-recipe
- work-in-progress
---

# PocketVision Chat, Code & Writing

**Status: source project and cloud training recipe only. No custom model weights or adapter have been trained or uploaded for this version.**

This project focuses on conversation, programming and source-based writing: explaining code, generating functions, debugging, neutral reference articles, and research outlines. Image upload and image understanding have been removed from the active application.

This repository is not yet a downloadable fine-tuned model or a running chat service. The supplied app uses the pretrained base until a compatible trained adapter is supplied. On a computer without a CUDA GPU it displays an interface preview and does not download the larger base weights.

## Selected base model

[Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507), Apache-2.0.
Pinned revision: `cdbee75f17c01a7cc42f958dc650907174af0554`.

The Qwen weights are supplied by the original publisher and are not re-uploaded here. The original PocketVision SmolVLM/ChartQA adapter cannot be used with this architecture.

## Planned training

The first proposed cloud experiment uses 20,000 examples from [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk), pinned to `5feaf2fd3ffca7c237fc38d1861bc30365d48ffa`:

- 50% `self-oss-instruct`: programming, primarily Python. Original source: BigCode, [self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k), ODC-BY.
- 30% `smol-magpie-ultra`: conversation and instruction following, Apache-2.0.
- 10% `smol-rewrite`: writing and editing, Apache-2.0.
- 10% `smol-summarize`: source summarization, Apache-2.0.
- 20 additional authored fictional article-format examples (12 train, 4 validation, 4 test); source packets stay in one split.
- Separate derived validation and test splits; duplicate opening prompts are removed.
- Assistant-only loss; 4-bit NF4 QLoRA, rank 16; one epoch as a starting experiment.

These are planned settings, not a description of completed training. Synthetic answers may contain errors. The base may already have seen these sources. The programming pilot does not establish broad coverage of other languages.

## Files and cloud workflow

- `app.py` and `chat.py`: text-only web and CLI entry points.
- `chatcode/`: runtime, data preparation, cloud trainer, tests, configuration, dependencies, and the detailed guide.
- [Cloud training guide](chatcode/README.md).
- [Cloud training notebook](chatcode/cloud_training.ipynb) (saved outputs are a local dry run only).
- The existing `pocketvision-chat-code-source.zip` is the original source snapshot before writing support. Use the current source files or the notebook's pinned snapshot download for this version.

## Articles and Wikipedia preparation

Reference article mode uses supplied source excerpts to draft a short neutral article with a lead, sections, references and research gaps. It rejects missing/unknown citation IDs and builds reference URLs only from supplied metadata. It does not fetch source URLs or verify factual entailment; each claim still requires human source review.

Wikipedia outline mode returns questions and source-review tasks for a human editor. English Wikipedia's [current guideline](https://en.wikipedia.org/wiki/Wikipedia:Writing_articles_with_large_language_models) restricts LLM-generated article prose, so reference drafts are intended for other writing contexts. Nothing is automatically published to Wikipedia.

The Wikimedia Wikipedia corpus is not included in the default mix: its cleaned articles have references removed and are not source-grounded instruction examples. The small authored examples use fictional practice topics and reserved URLs, not genuine research references.

Run the following on a remote Linux CUDA GPU, from the source directory:

```bash
python -m pip install torch==2.9.1 --index-url https://download.pytorch.org/whl/cu128
python -m pip install -r chatcode/requirements-cloud.txt
python -m pip check
python -m chatcode.data --output /checkpoints/chatcode-data
python -m chatcode.train --data /checkpoints/chatcode-data --output /checkpoints/smoke --max-steps 25
```

Use mounted persistent storage for `/checkpoints`. Inspect the smoke run before estimating and launching a full experiment. No paid hardware is started by uploading this repository. The trainer refuses CPU training and does not upload automatically.

## Verification and limits

Offline checks cover assistant-only labels, source-packet separation, malformed and invented references, text-only validation, CPU refusal before model download, data integrity, and a forward/backward check with a tiny randomly initialized Qwen architecture. These checks do not train or evaluate the selected 4B model. The Colab notebook's Python cells were executed only in dry-run mode; its full GPU path is unverified.

The cloud dependencies and full GPU training run still need a cloud smoke test. No chat-quality or programming-accuracy improvement is claimed. A trained candidate needs separate answer review and isolated code tests before release. This repository does not include a sandboxed code-execution evaluator.

## Licenses and attribution

Project source: GPL-3.0-only (`LICENSE`). Original Qwen base: Apache-2.0 (`LICENSE-BASE-MODEL.txt`). Dataset and dependency licenses remain applicable. See the training guide and configuration for provenance. No pretrained base weights, raw datasets, credentials, or original vision adapter are included.